Getting the right people on the big data bus

By Jill Dyché, VP, SAS Best Practices

In their book Team Genius, authors Rich Karlgaard and Michael S. Malone pose the question:

Is your organization, and the teams that compose it, up to the challenges they face in a hypercompetitive global economy?

Followed by:

If not, is there some way to accelerate your understanding of teams?

If you’re grappling with the challenge of launching a big data initiative, and if the answer to either of these questions is "No," then you’ve come to the right place. Big data isn’t just changing the technology and development landscape at companies. It’s forcing executives to reexamine incumbent organizational frameworks and cultural norms. It’s inviting new conversations around not only data and systems, but people. And it’s about time.

Many companies have wasted no time installing new big data technologies (like Hadoop), loading it up with data, and then asking, “How can this data help us?” That’s backwards.

Anne Buff and Tamara Dull have answered the most important questions about big data teams in their new white paper, Getting the Right People on the Big Data Bus.

I recently asked to sit down with them — they both work for me, so they both agreed — to talk a bit about what it takes to assemble a skilled-yet-agile big data team.

We’ll get into the roles in a second. But at a more macro level, what’s different about big data teams?

Tamara Dull: I view big data teams as a natural evolution of our tried-and-true, traditional BI/DW teams. The key difference is that with so much data readily available these days, it’s easy for these teams to get overwhelmed and focus on the wrong things. Many companies have wasted no time installing new big data technologies (like Hadoop), loading it up with data, and then asking, “How can this data help us?” That’s backwards. Today’s big data teams need to identify the business issues first, then figure out what data and technologies will best support those requirements.

Anne Buff: Big data teams challenge our beliefs and paradigms about organizational frameworks. Rather than being built from reporting structures and job titles and following traditional org charts, big data teams are built based on specific project needs and objectives. Members span organizational divides and take on various roles and responsibilities as the project rolls out. Once a team’s project is complete (or terminated – which is not a bad thing, especially in the world of big data), the team dissolves. New project, new objectives, new team.

You both write about the job roles necessary for big data success. Some of them, like the data scientist, are new to many companies. Others, like the business analyst, have been around a while. Where should managers looking to staff big data teams start?

TD: I have two quick pointers. First, let your company’s pressing business issues drive what skills and roles are needed. If you don’t know what these issues are, start there. Second, you don’t have to tackle big data on your own. Use external resources — such as technology vendors, freelance developers, big data consultants, Hadoop solution providers, etc. — to help fill in the gap as your company builds out its big data skill sets.

AB: When it comes to big data teams, interest and excitement are huge drivers. You will find unexpected gems in team members who are motivated by the project goals and objectives. Whether business or IT (note: this delineation dissipates quickly on well-built teams), established employees find big data projects a great place to contribute skills they don’t otherwise get to use in their daily roles. Rather than selecting individuals for a big data project team, post the goals and objectives of the project and solicit interest. You will be pleasantly surprised by the organically built team.

When it comes to big data, there’s a chicken-and-egg relationship with analytics. You could argue that companies can’t really take their analytics programs to a new level until they’ve reached a certain level of big data maturity. On the other hand, you could argue that analytics is big data’s raison d’etre. Where do you two stand in this debate?

TD: Big data is about money — saving it and making it. With today’s big data technologies, companies are now able to store and process any and all types of data — at a fraction of the time and cost of traditional relational technologies. That’s the saving money part. To make money, however, companies need the flexibility to ask their data any question, as well as discover new questions and avenues to explore. This is what big data analytics is all about.

AB: Maybe bad form here, but I am going with another idiom. Go with the bird in the hand. Whichever has the attention of executives is what wins. Big data has certainly brought the limelight that all data has long-since deserved. But many executives are quick to say, “Why do we need big data (or more data) when we don’t use what we already have?” The good news is analytics and big data programs can evolve together. They can coexist as symbiotic divisions or evolve into a cohesive information powerhouse. The success of big data and analytics, regardless of which comes first, is critically dependent upon executive leadership and the culture which they foster.

As you know, our team gets a lot of questions about nascent big data programs. What big data question do you hear most often, and how does getting the right people on the big data bus answer it?

TD: The most common question used to be, “Where do we even begin?” This paper lays out a 9-step roadmap that not only helps those new to big data, but also provides a helpful checklist for those who may have gotten off to a rocky start. For example, one of the steps is about building the right team; the second half of the paper focuses on how to do just that.

AB: The most common internal debate we hear is, “Who owns or should own big data?” Typically this question means who has the company voice in where big data is going – IT or “the business” (lovingly referring to anything not IT). I am so excited to say that this paper may bury that question once and for all. Big data owners, sponsors and stakeholders will change with specific projects. Individual contributors will come and go based on needs. Businesses must be flexible and recognize that big data programs do not live in one specific place on an organizational chart but are instead a dynamic integration of diverse skill sets from across the entire company.